Method of determining a device spectral response

ABSTRACT

A method is provided of determining a device spectral response for an image recording device. The method comprises obtaining a filtered light response from a number of filters of a filter set, wherein each filter has a pass band with at least one boundary defined by a transition region. In the wavelength region of interest adjacent transition regions of the same sense are substantially non-overlapping. The obtained filtered light response for each filter is stored as response data. The device spectral response is determined using the stored response data and separately determined data describing the spectral response of the number of filters.

FIELD OF THE INVENTION

The present invention relates to a method of determining a devicespectral response, for example in an imaging recording device such as ascanner or digital camera.

DESCRIPTION OF THE PRIOR ART

The use of scanners is well known to convert information upon a scannedmedium into electronic form. Accordingly, scanners are wide spread inthe photographic and image processing industries, in addition to beingcommon in personal computing applications for domestic users. Oneimportant application of scanners is in the accurate reproduction ofcolour information contained upon a medium such as that of a colourimage.

Unfortunately, the data representing uncorrected information from ascanned medium is generally not a colour accurate reproduction of theoriginal. This is particularly problematical in colour scanners wherethe detection of colour information is usually achieved by the use of alight detector in combination with a number of colour filters. Forcolour scanners, the scanner integrates the intensity of the lightreceived over an area of the visible spectrum of the scanner to producea single value of the integrated intensity in that area. Typically threevalues are measured using three colour filters (red, green and blue).

Accordingly, for a particular point on the medium, the intensity oflight received over the spectrum through each filter is recorded. Thisproduces three values R,G,B corresponding to the red, green and bluefilters and these “RGB” values represent the colour of the particularlocation of the medium as “seen” by the scanner.

Each of the three colour filters has a characteristic spectral response.The overall scanner response is also effected by the light source (whichhas an associated spectrum), the scanner optics and the light detector.

The standard approach to overcoming the lack of colour accuracy is bythe use of colour charts, the idea being to produce a chart having“patches” of various colours and then to scan this chart and store thevarious R,G,B data values which are produced.

Each of the chart patches has a corresponding “true” colour which may bedefined in accordance with industry standards by “X,Y,Z” or “L,A,B”values. These define a particular colour and therefore the desire is torelate these values to those produced by the scanner using the R,G,Bfilters. The conventional approach to achieve this, is to scan thepatches upon a colour chart and then form a transformation of the datain order to describe the relationship between the X,Y,Z/LAB values andthe R,G,B values. This transformation forms the basis of an inputprofile or characterisation for the scanner.

One problem with this is that the colour produced by the chart dependsupon not only the medium on which the chart is prepared but also thedyes used in the creation of the various colours (as usually a smallnumber of dyes are used in combination to produce a particular colour).

As a result, the correspondence between the X,Y,Z and R,G,B values doesnot readily generalise to any medium and indeed it also depends upon theactual constitution of the colour chart patches selected. Colour chartsproduced by different manufacturers therefore have differentcorresponding relationships relating the XYZ/LAB visual colours to theR,G,B values produced by the scanner. There is therefore a desire toovercome these problems and produce a method which is substantiallyindependent of the colour chart used to generate the scanner profile andtherefore will generalise more readily.

In particular, it is desirable that such a method is accurate and rapidas each scanner has apparatus variations and therefore the method shouldbe applied to each scanner individually. Similar comments apply to otherimage recording devices.

SUMMARY OF THE INVENTION

In accordance with a first aspect of the present invention, we provide amethod of determining a device spectral response for an image recordingdevice, the method comprising:

obtaining a filtered light response from a number of filters of a filterset, wherein each filter has a pass band with at least one boundarydefined by a transition region, wherein in the wavelength region ofinterest, adjacent transition regions of the same sense aresubstantially non-overlapping;

storing the obtained filtered light response for each filter as responsedata; and

determining the device spectral response using the stored response dataand separately determined data describing the spectral response of thenumber of filters.

According to the present invention, the identification of the devicespectral response is provided by the use of the filter set which allowsthe response at different wavelengths to be analysed. In addition, theactual spectral responses of the filters are separately determined andthis information is used in conjunction with the response (for example,obtained values) stored according to each filter, in order to determinethe spectral response of the device. Examples of such image recordingdevices include scanners and digital cameras.

The use of such a response is particularly advantageous for scanners inthat it reduces the time required to generate scanners characterisations(profiles) for various media by removing the need to scan the individualmedia on the target device.

The determined “device spectral response” in fact typically comprises anumber of individual and independent responses. We have realised thatthe commutative nature of the contributions from the various apparatuswithin the scanner or camera allows these contributions to be treated asa unitary response.

The response of the detector is generally included in this unitaryresponse along with responses from the device optics such as any lenses,mirrors and other filters thus obtaining an “overall” device spectralresponse encompassing the entire characteristic of the device. Inparticular for scanners, the device spectral response preferablyincludes a spectral response of the scanner light source.

In the case of colour devices, separate device spectral responses aretypically determined for one or more of a red, green or blue scannerchannel respectively. Typically three colour channels as used such asred, green and blue channels respectively (although other numbers ofchannels could be used).

A separate device spectral response is preferably determined for eachchannel used. Red, green and blue device spectral responses can bedetermined using the corresponding red, green and blue filters and thedevice spectral responses for each may include the spectral responses ofthese filters as part of the unitary response. The separate responsescorresponding to these three colours could be obtained using threeseparate images or scans, although typically they are obtained in asingle step by dividing the incident light from the filters and passingit through the individual colour filters, for example using a beamsplitter.

In addition rather than performing a separate scan or obtaining aseparate image for each of the number of filters in the filter set,these filters may be arranged such that the response is obtained foreach filter in a single step. For example this may be achieved in ascanner by arranging the filters in an array upon the scanner platen.

The separately determined data describing the spectral response of thenumber of filters of the filter set may be represented discretely as amatrix, such that for each filter, the spectral response is described asa series of values across the spectrum and wherein the matrix is formedfrom the said discrete values for each filter of the number of filters.

The inverse of this matrix in conjunction with the scanned values forthe filter set can then be used in determining the device spectralresponse. Typically this is achieved by multiplying the obtainedresponses (values) for the filters by the inverse matrix of the spectralresponse of the said filters.

The device spectral responses for each colour are generally representedas vectors having an identical number of components to the number ofcomponents used in the representation of the filter spectra.

A number of mathematical techniques are available for use in invertingthe spectral response matrix and in particular singular valuedecomposition methods have been found to generate favourable results.These methods may be conveniently performed upon a computer system suchas a desktop PC or a dedicated onboard computer within a scanner/camera.

Although one location for each filter from the filter set may be used toperform the method, a more accurate result may be achieved by using anumber of locations for each filter.

The separately obtained spectral measurements can be made by placing thefilters within apparatus such as a spectrophotometer. Again greateraccuracy can also be achieved here by using a number of measurements atdifferent locations across the filters.

Once the device spectral response has been determined it can then beused in a number of ways.

In particular, in accordance with a second aspect of the presentinvention a method of predicting recorded data for an image recordingdevice is provided, the recorded data describing the appearance of anumber of locations in a target image. The method comprises:

determining data describing a device spectral response using a methodaccording to the first aspect of the invention;

obtaining spectral data describing the appearance of each of thelocations in the image; and, processing the spectral data in accordancewith the determined data to generate the predicted recorded data.

The spectral data for the target image may be obtained using apparatussuch as a spectrophotometer as described above. For scanners, anyscanned medium can be used as a source of the target image althoughtypically the scanned medium is a colour chart, such as one of thenumber of colour charts used within the art. Each patch upon such acolour chart may be used to generate spectral data. Typically, the stepof processing the spectral data comprises convoluting the spectral datawith the determined data in the form of the overall device response. Forcolour scanners, the predicted data are generally presented in the formof R,G,B values.

The ability to accurately predict the R,G,B values removes the need fornumerous scans of different media types to be performed. In addition,having effected the separation of the device spectral response, theability to predict R,G,B values can be used with a known method ofconverting these values into LAB or XYZ values. As a result, anadditional step of determining a relationship between the predicteddevice values and the LAB or XYZ values can be performed, so as togenerate a device profile, or characterisation. This relates what thescanner sees in RGB values in device dependent space into a visual,device independent space i.e. LAB.

As the scanner (device) spectral response is independent of the mediumscanned, profiles can be generated for known colour charts (previouslyused in the characterisation of scanners) without performing actualscans. The only data needed to generate such profiles is accuratespectral data from each area of the colour chart and the device spectralresponse. Using the combination of the device response and the spectraof the media, scanner values in RGB may be synthesised and patch LABvalues may be determined directly from the patch spectra. Thus usingstandard techniques common in the art a transformation may be generatedrelating device dependent RGB values to device independent LAB values ina scanner profile or characterisation. The ability to form a transformbetween LAB/XYZ and RGB values for a scanner, without the need toindividually scan the media therefore reduces the time and expense ofgenerating profiles. Digital cameras can also be characterised in thisway.

The accuracy with which the device spectral response may be determinedis in part dependent upon the filter set used. Therefore in accordancewith a third aspect of the present invention we provide a filter set foruse in determining a device spectral response for an image recordingdevice. Each filter has a pass band with at least one boundary definedby a transition region, wherein in the wavelength region of interest,adjacent transition regions of the same sense are substantiallynon-overlapping. The transition regions of the filters are arranged tobe substantially non-overlapping (in wavelength) as overlapssignificantly reduce the accuracy of the results achieved.

A number of different filter types can be used. For filter sets havingfilters in which a single transition region is present in the wavelengthregion of interest, the transition regions of the filters are preferablyarranged adjacent one another within this region.

In this case, preferably each transition region is bounded by upper andlower wavelengths and the filter set is arranged such that the upperwavelength of one transition region and the lower wavelength of anadjacent transition region are at substantially the same wavelength.

Examples of such filters include high pass wavelength filters in whichwavelengths of light longer than those within the transition region aresubstantially passed by the filter whereas wavelengths of light shorterthan those within the transition region are substantially blocked.Alternatively, low wavelength pass filters can be used such thatwavelengths of light smaller than those within the transition region aresubstantially passed by the filter whereas the wavelengths of lightlonger than those within the transition region are substantiallyblocked.

A further alternative is to use filters having more than one transitionregion, such as band pass filters. In this case each filter has a passband within upper and lower wavelength boundaries, each boundary beingdefined by corresponding upper and lower transition regions of oppositesenses, each transition region being within the wavelength region ofinterest. Each filter of this kind typically has an upper and lowertransition such that only light of a wavelength between the upper andlower transitions is substantially passed by the filter whereas light ofother wavelengths is blocked.

Preferably the filters are arranged such that their corresponding passbands are substantially adjacent in the wavelength region of interest.For adjacent filters, the transition region of one filter is thereforepreferably adjacent to the transition region of the opposite sense foran adjacent filter.

In general when filters of similar type are used within a filter set,the transition regions of the same sense for each of these filters arepreferably substantially equally spaced in wavelength with respect toone another. In this way the part of the optical spectrum of interestcan be spanned evenly.

For all transitions, it is desirable that the transition as a functionof wavelength between the effect of allowing light to pass and to beblocked is as rapid as possible. This allows an increased number offilters to be used in that the transition between the filtering (block)and non-filtering (pass) effect is limited to a very narrow wave band.

Depending upon the arrangement of the particular scanner, the filtersmay be arranged as either transmissive or reflective filters.

In accordance with a fourth aspect of the present invention the methodaccording to the first or second aspects of the invention is performedusing a filter set in accordance with the third aspect of the presentinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

An example of the method according to the present invention will now bedescribed with reference to the accompanying drawings, in which:

FIG. 1 is a schematic representation of a scanner;

FIG. 2 shows a typical spectrum for a light source;

FIG. 3 shows a typical spectrum from a medium;

FIG. 4 illustrates typical R,G,B and infra-red filter spectralresponses;

FIG. 5 a shows the combined spectrum obtained through the red filter;

FIG. 5 b shows the combined spectrum obtained through the green filter;

FIG. 5 c shows the combined spectrum obtained through the blue filter;

FIG. 6 illustrates the combined responses from the scanner;

FIG. 7 shows a schematic representation of a scanner arranged to scan anarray of filters in a filter set;

FIG. 8 shows the spectral responses of a filter set;

FIG. 9 is a flow diagram of the method;

FIG. 10 represents the spectra from the IT8.7 data set;

FIG. 11 shows the generation of RGB values; and,

FIG. 12 shows the method of comparison between predicted and measuredvalues.

DETAILED DESCRIPTION

FIG. 1 is a schematic illustration of the main components of a knownscanner apparatus, generally indicated at 1. A light source 2 isprovided which illuminates a medium 3, the medium 3 in this case being aphotograph.

As is known, scanners can have either transmissive or reflectiveconfigurations, such that the light from the light source is eitherpassed through or reflected from the medium 3 respectively.

In FIG. 1, a transmissive scanner is shown in which light transmittedthrough the medium 3 is analysed. The transmitted light passes through alens or lenses 6 and impinges upon a beam splitter 8. The beam splitter8 divides the incoming light beam into three beams which are thendirected through three transmissive colour filters 9.

The three colour filters 9 are provided in the form of red, green andblue filters. These filters are arranged to only allow the passage oflight having wavelengths within the corresponding spectral ranges ofred, green and blue light. Each of the three filtered light beams thenimpinges upon detectors 10 where the intensity of the component beams isconverted into corresponding data and these data are eventually outputby the scanner as red, green and blue values respectively.

The spectra of the light beams from each of the filters 9, when measuredat the detectors 10, contains spectral contributions from a number ofthe components within the scanner, and in particular the light source 2,the medium 3 (this being the spectrum of interest) and one of the colourfilters 9.

Referring now to FIG. 2, a typical spectrum from the light source 2 isshown. It can be seen immediately that this contains characteristicpeaks and that therefore the intensity of the emitted light at somewavelengths is much greater than that at others.

In FIG. 3, a spectrum 12 is indicated which represents the spectrum of aparticular point upon the medium 3 assuming it were illuminated withincident light having a constant intensity at all wavelengths (purewhite light).

The spectral response of typical red, green and blue filters 9 isindicated at 13, 14, 15 respectively in FIG. 4, again using pureincident white light. In addition, a further filter is often providedwhich blocks most of the incident infra-red wavelengths, that is thosegreater than 700 nanometres. This prevents unwanted heating of theapparatus and infra-red light outside the visible spectra affecting thered response.

The spectral response of this filter is shown at 16 in FIG. 4. In somecases an additional ultra-violet filter is also provided (not shown).

As a result of the combination of the spectral responses of the lightsource 2, medium 3, and the filters 9, including any additionalinfra-red or ultra-violet blocking filters, three overall spectracorresponding to the three colour filters 9 are presented to thedetector(s) 10 of the scanner 1.

Examples of these three spectra are shown in FIGS. 5 a, 5 b or 5 c forthe red, green and blue filters respectively. It can be seen in FIG. 5 cthat very little light is incident upon the detector 10 for the bluefilter 9 as would be expected when the spectrum of FIG. 3 is consideredas this has little intensity at shorter wavelengths.

The detector 10 operates by effectively integrating the total lightintensity received for all light wavelengths across the visiblespectrum, in this case being from 400 to 700 nm. A separate integrationis performed using each of the three spectra according to the red, greenand blue filters 9. Examples of the resultant spectra 17, 18, 19 for thethree filters are shown in FIGS. 5 a, 5 b and 5 c for the red, green andblue filters respectively.

The integration over the spectrum is therefore the area under therespective spectral curves and each of these is given a single valuecorresponding to the respective filter, so as to provide the “RGB”values in accordance with the art.

It should be remembered that each of the combined spectra shown in FIGS.5 a, 5 b and 5 c includes not only the responses from the light sourceand filters, but also that of the medium 3.

The scanner spectral response is illustrated in FIG. 6 where theindividual contributions according to the light source 2, lenses 6, red,green and blue filters 9 and detectors 10 are shown as spectra 20, 21and 22 respectively. The spectra when multiplied by the spectrum 12 fromthe medium 3, produce the three corresponding overall responses shown inFIGS. 5 a, 5 b and 5 c.

With respect to the known scanner described above, the example of thepresent invention replaces the medium 3 with a number of filters asshown in FIG. 7. Each filter 7 belongs to a filter set and these arearranged in an array such that light from the light source 2 is passedthrough each of the filters 7 individually during a single scan. Thescanned values obtained are then processed along with the filter spectrato determine scanner response.

The spectral responses 30 of a suitable filter set having a number offilters 7, are shown in FIG. 8. In this case, each filter is a high pass(blocking) filter.

As can be seen, each filter exhibits a rapid transition between a verylow transmissivity (blocking light) and a high transmissivity (passinglight). This transition occurs over a few nanometres in wavelength. Athreshold 31 in wavelength can be defined for each filter which in thiscase represents a transmissivity of substantially one half of themaximum value obtained by the filter.

As shown in FIG. 8, the filters 7 within the filter set are chosen suchthat their threshold values are spaced across the range of interest ofthe optical spectrum, this being between 400 and 700 nanometres in thiscase.

Using this filter set, a suitable method of de-convoluting the scannerspectral response is shown in FIG. 9.

At step 100, the filter set array is loaded into the scanner.

A scan is then performed of the array of N filters at step 101. In doingso, light from the light source 2 passes through the filters 7, impingesupon the beam splitter 8 and passes through the colour filters 9 to thedetectors 10. Corresponding individual R,G and B values representing theintegration of the component spectra shown in FIGS. 5 a, 5 b, 5 c, arethen recorded for each of the N filters 7 of the array.

The finite dimensions of each filter 7 are used to advantage in thatR,G,B values are determined for a number of locations within eachfilter. These are “seen” by the detector as pixels and therefore theR,G,B values for a particular filter 7 are averaged (using the data froma number of these pixels) in order to produce a more representativevalue in each case. The scanned values are then stored at step 102.

For a particular point (seen as a pixel) on the filter 7, the R,G,Bvalues can be represented mathematically as:

$\begin{matrix}{R = {{\int_{400\mspace{11mu}{nm}}^{700\mspace{11mu}{nm}}{{\Phi(\lambda)}{R_{S}(\lambda)}\mspace{14mu} G}} = {{\int_{400\mspace{11mu}{nm}}^{700\mspace{11mu}{nm}}{{\Phi(\lambda)}{G_{S}(\lambda)}\mspace{14mu} B}} = {\int_{400\mspace{11mu}{nm}}^{700\mspace{11mu}{nm}}{{\Phi(\lambda)}{B_{S}(\lambda)}}}}}} & (1)\end{matrix}$Where:

-   Φ is the spectral transmittance (or reflectance) of the point of the    filter 7 in question as a function of the light wavelength λ, and    R_(S),G_(S),B_(S) are the red, green and blue scanner spectral    responses respectively, again as a function of wavelength, using the    corresponding red, green and blue filters 9. As indicated by the    integral limits in Equation 1, each of the R,G,B values is    determined by integrating over the “visible” wavelength, which for    this example is between 400 and 700 nm.

For calculations using real data, the above integrals are converted intodiscrete mathematics. The spectral response Φ of each filter 7 can berepresented as a vector of order M, where M is the number of samplestaken across the spectrum (from 400 to 700 nm here). The samples aretaken at constant intervals across the spectrum and represent theintensity of the response at a particular wavelength (or over a range ofwavelengths). The wavelength range may be equal to the difference inwavelength between the sampling points.

One method of obtaining the values for the matrix Φ is to measure theoptical properties of the filters 7 directly using apparatus such as aspectrophotometer. In this example, for each of the N filters, M valuesare used spanning the spectrum, the values taking the form of averageintensity values for the spectrum in wavelength ranges, such as 10 nm(400–409 nm for example). This produces 31 discrete values for theoptical wavelengths of interest. These data are obtained at step 103.

As there are N filters 7, and M measurements for each filter, then thisinformation may be represented as an “N by M” matrix Φ, as set outbelow:

$\begin{matrix}{\Phi = \begin{pmatrix}\phi_{1,1} & \cdots & \cdots & \phi_{1,M} \\\cdots & \cdots & \cdots & \cdots \\\cdots & \cdots & \phi_{i,\lambda} & \cdots \\\phi_{N,1} & \cdots & \cdots & \phi_{N,M}\end{pmatrix}} & \lbrack 2\rbrack\end{matrix}$

The matrix Φ therefore contains all of the data describing the spectralresponses of the N-filter array. The three spectral responses of thescanner (which are media independent) (that is for the red, green andblue filters 9), can be represented as three corresponding vectors ofdimension “M”:R_(S)=(R₁, R₂, . . . , R_(λ), . . . , R_(M))  [3]G_(S)=(G₁, G₂, . . . , G_(λ), . . . , G_(M))  [4]B_(S)=(B₁, B₂, . . . , B_(λ), . . . , B_(M))  [5]These vectors represent the scanner spectral responses in terms ofdiscrete values across the spectrum as a function of wavelength.

A set of R, G and B values, as determined by the scanner, are producedfor each filter 7. Therefore the number of R, G and B values is N. Thevalues for the R filter 9 can be represented as an N component vector,and similarly for the G and B values. Specifically, these can berepresented as:R_(V)=(R_(V,1), R_(V,2), . . . , R_(V,N))  [6]G_(V)=(G_(V,1), G_(V,2), . . . , G_(V,N))  [7]B_(V)=(B_(V,1), B_(V,2), . . . , B_(V,N))  [8]where each component R_(V,1) etc, actually represents an average valueof a number of pixels within the scanned area for a particular filter 7.

Equation 1 can therefore be rewritten in terms of the discrete Equations2 to 8 as:R_(V)=ΦR_(S) ^(T)  [9]G_(V)=ΦG_(S) ^(T)  [10]B_(V)=ΦB_(S) ^(T)  [11]where the “T” superscripts represent the transpose conjugate of thevectors R_(S),G_(S),B_(S).

Each of the component values of R_(V), G_(V), B_(V) are obtained byscanning the filters 7 with the scanner. The values of the matrix Φcomponents are also required in order to determine the scanner spectralresponses R_(s), G_(s), B_(s). Rearranging Equations 9 to 11, thescanner spectral responses are given as:R _(S) ^(T)=Φ⁻¹ R _(V)  [12]G _(S) ^(T)=Φ⁻¹ G _(V)  [13]B _(S) ^(T)=Φ⁻¹ B _(V)  [14]where Φ⁻¹ is the inverse matrix of Φ. These equations are then solved atstep 104.

As will be appreciated, in general it will not be possible to generatean analytical solution to Φ⁻¹ since the matrix Φ may not be square andis filled using experimental data.

However, one of a number of known approximate mathematical methods maybe used to solve the inverse matrix. Suitable methods include that ofperforming a “pseudo” inverse for example as described in “NumericalRecipes in C, The Art Of Scientific Computing”, by William H. Press,Saul A. Teukolsky, William T Vetterling and Brian P Flannery, Publishedby Cambridge University Press, ISBN 0-521-43108-5 which estimates theinverse using a Gaussian elimination method. Another method is to use“least squares” although this encounters problems with negative values.Negative values are not practically possible in a filter, so thesolution may be mathematically correct but not represent a real filter.

A particularly successful method is that of singular value decomposition(SVD). Using this technique, the matrix Φ can be divided into threematrices as:Φ=[U].[W].[V]  [15]where W is a diagonal matrix of the same dimension as the matrix Φ, withnon-negative diagonal elements in decreasing order. U and V are unitarymatrices. This gives:Φ⁻¹ =[V].[1/W].[U] ^(T)  [16]

If Equations 12 to 14 are rewritten in terms of Equation 16, then ansolution can be found for the scanner responses

R_(S)^(T), G_(S)^(T), B_(S)^(T).The actual method used in the present example is an extension to the SVDprocess called “SVD Fit” as described in “Numerical Recipes in C”,referenced above. This method is similar to that of SVD except that ithas the ability to weight the solution. This weighting is useful in thepresent example as the spectra from the red, green and blue filters 9can therefore be limited to appropriate parts of the spectrum. The SVDfit method directly produces a solution for

R_(S)^(T), G_(S)^(T), B_(S)^(T).

As will be appreciated, it is beneficial to place a tolerance upon theEigen values in W. Small Eigen value contributions will dramaticallyaffect the solution of Equation 16 (due to the “1/W” term). In addition,it is possible to reduce the resultant high frequency noise (caused byhigher Eigen vectors and values) by ignoring some of these highervalues. An analysis of the contribution of the Eigen vectors to thefinal answer is therefore recommended. A combination of removing thesmall values in W, selecting only a number of the vectors and assessingthe contributions from these vectors, leads to a stable solution bearingin mind that an oversimplification rejecting too many Eigen vectors willcause inaccuracies in the results. Therefore a balance should bemaintained between removing noise on the one hand and maintainingaccuracy on the other.

It will be appreciated that, the method will be generally implementedusing a computer system. In this case a desktop PC can be programmed toperform the calculations associated with the method.

A number of known data processing techniques used in scanners caninfluence the accuracy of the results. For example, “gamma correction”is usually applied to scanned images in the form of a transfer function,as it is known that such gamma correction produces a more pleasing imageto the eye when viewed on a screen.

The response data is also conventionally “logged” in order to compressthe data range and this can affect the accuracy of the results due tothe accuracy with which the logged data is recorded. Additional furtherprocessing is also sometimes performed upon the data.

In general, for the present method, it is desirable to use the data inthe unprocessed or raw form, that is “linear” data in “transmissionspace”, however, logging and gamma correction can generally be invertedto obtain transmission data.

Noise in the data should also be minimised in order to improve theaccuracy of the results. Typically noise is a direct result of theaccuracy to which the data are measured, the truncation of data andrandom noise within the system (such as due to the electroniccomponents). It has been found that some filter sets are more suitablethan others when attempting to reduce noise within the system. This ismainly due to the sharpness of the filter and the spacing of the filterset, also the quality of the filter and hence its uniformity can help toreduce noise.

In order to test the method, several different filter sets were examinedfor their ability to recover scanner response. Each filter set was usedto create a filter array which was then scanned to recover the scannerspectral response.

Once the scanner spectral response was determined, this was then used topredict the response of the scanner to the IT8.7 data set using thespectra of the patches shown in FIG. 10.

As shown in FIG. 11, the predicted RGB values for the IT8.7 data setwere obtained by convolving the spectra from each patch with the scannerspectral response determined using the different filter sets.

Using spectra rather then real charts/paint sets is not a completelyrealistic analogue as, aside from the scan variations mentioned above,noise is always present within the scanner systems and this influencesthe results. In order to take account of this, an amount of mathematicalrandom noise was added to the data to make the results more realistic. Aset of “measured” RGB values were then calculated for the “measured”scanner response spectra.

The predicted RGB values were then compared with the measured RGB valuesfor IT8.7/3 charts and paint sets. In each case the RGB value wasconverted to an approximate LAB value to enable a “Delta E” number to becalculated. This gives a measure of the quality of recovery upon realdata. The overall process is summarised diagrammatically in FIG. 12.

The predicted RGB values were then compared with the measured RGB valuesfor IT8.7/3 charts and paint sets. In each case the RGB value wasconverted to an approximate LAB value to enable a “Delta E” number to becalculated. This gives a measure of the quality of recovery upon realdata. The overall process is summarised diagrammatically in FIG. 12.

A major advantage of the invention is that, because the scanner spectralresponses are independent of the medium scanned, once the responses havebeen evaluated for a scanner, further scans of new media are notrequired. The “characterisation” of the scanner can therefore beeffected using a single scan of the filter set. It is not necessary toperform any further scans as these can be represented with datadescribing their spectra. The spectral data can be supplied to thecustomer along with the scanner, where the customer may scan the filterset and determine the scanner response, and hence may generatecharacterisations for the various media.

This also conveniently overcomes the problems associated with changes inthe optical properties of colour charts due to ageing. In addition, fornew media, the scanner response can be convoluted with the spectra ofthe new media, to produce RGB values. A set of “predicted” RGB valuescan therefore be generated for the new media and hence newcharacterisations. This additional step is shown at step 105 in FIG. 9.

As is known, a spectrum can be used to generate X,Y,Z or L,A,B valuesfor the particular colour in question. Therefore, using spectral datafor media, it is possible to generate L,A,B (if these are unknown) andcorresponding R,G,B values without performing the scans which wouldordinarily be required.

This leads directly to the ability to generate an input profile for thescanner which establishes a relationship between the L,A,B and R,G,Bvalues without performing scans upon the new media. Conventional methodsfor determining the relationship between these values can be used. Thisis shown at step 106 in FIG. 9.

It is however important that the method is as accurate as possiblebecause the input profile is generated based upon the predicted ratherthan the scanned R,G,B values.

As a result of experimentation it has been found that the filters shouldhave very narrow transitions in wavelength and that there should beminimal overlap between transitional regions of the filters. The visiblespectral range should ordinarily be covered by these filters.

It can be seen that the present method may be employed to obtain thescanner spectral responses with sufficient accuracy to enable RGB valuespredictions to be made and a corresponding scanner profile generated.

Although an example of the invention has been described above inconnection with a scanner, it will be appreciated that analogous filtersand methods can be used in order to determine device spectral responsesfor other image recording devices. In particular these include cameraswhich generate electronic images such as digital cameras.

1. A method of determining a device spectral response for an imagerecording device, the method comprising: obtaining a filtered lightresponse from a number of filters of a filter set, wherein each filterhas a pass band with at least one boundary defined by a transitionregion, wherein in the wavelength region of interest, adjacenttransition regions of the same sense are substantially non-overlappingin wavelength with respect to each of the other filters; storing theobtained filtered light response for each filter as response data; anddetermining the device spectral response using the stored response dataand separately determined data describing the spectral response of thenumber of filters; wherein the step of determining the device spectralresponse comprises describing the spectral response of the number offilters of the filter set as an N by M matrix with N being the number offilters, such that for each filter, the spectral response is describedas a series of M sampled values across the spectrum and wherein thematrix is formed from the said values for each filter of the number offilters, and multiplying the values representing the obtained filteredlight response by the inverse matrix of the spectral response of thesaid filters, said inverse matrix being determined using singular valuedecomposition.
 2. A method according to claim 1, wherein the imagerecording device has a light detector and wherein the determined devicespectral response includes a spectral contribution from the lightdetector.
 3. A method according to claim 1, wherein the obtainedresponse contains color information.
 4. A method according to claim 3,wherein separate device spectral responses are determined for one ormore of a red, green or blue channel respectively.
 5. A method accordingto claim 4, wherein the red or green or blue device spectral responsesare produced using corresponding red, green or blue filters and whereinthe scanner spectral responses include the spectral contributions fromthe said red, green or blue filters.
 6. A method according to claim 1,wherein the step of obtaining a filtered light response from a number offilters is performed with a scanner and wherein the device spectralresponse is the scanner spectral response.
 7. A method according toclaim 1, wherein the device spectral response is reconstructed using aselected number of Eigen vectors from the inverse matrix.
 8. A methodaccording to claim 7, wherein the selection of the Eigen vectors isbased upon their contribution to the device spectral response.
 9. Amethod according to claim 7, wherein the selection is based upon theorder of the Eigen vectors.
 10. A method according to claim 7, whereinthe selection is based upon the magnitude of the Eigen vectors.
 11. Amethod according to claim 1, wherein the device spectral response isdetermined using an iterative method.
 12. A method according to claim 1,wherein the obtained response, is determined by the total amount oflight received over at least part of the spectrum.
 13. A method ofpredicting recorded data for an image recording device, the recordeddata describing the appearance of a number of locations in a targetimage, the method comprising: determining data describing a devicespectral response using a method according to claim 1; obtainingspectral data describing the appearance of each of the locations in theimage; and, processing the spectral data in accordance with thedetermined data to generate the predicted recorded data.
 14. A methodaccording to claim 13, wherein the step of obtaining the spectral datacomprises determining the spectral data using a spectrophotometer.
 15. Amethod according to claim 13, wherein the step of processing thespectral data comprises convoluting the spectral data with thedetermined data.
 16. A method according to claim 13, and when dependentupon claim 3, wherein the predicted scanned data is in the form of R, G,B values.
 17. A method according to claim 13, further comprising thestep of determining a relationship between the predicted recorded dataand the spectral data obtained so as to generate a recording deviceprofile.
 18. A method according to claim 13, wherein the spectral dataare represented in the form of LAB or XYZ values.
 19. A method accordingto claim 13 wherein the image recording device is a scanner and whereinthe target image is obtained by scanning a target medium.
 20. A methodaccording to claim 19, wherein the scanner has a light source andwherein the determined device spectral response includes a spectralcontribution from the scanner light source.
 21. A method according toclaim 19, wherein each of the number of filters of the filter set isscanned during a single scan.
 22. A method according to claim 1, whereinthe image recording device is a digital camera.
 23. A method accordingto claim 21 for determining a scanner spectral response, wherein thesaid filters used in the step of scanning a number of filters comprise afilter set of filters each having a pass band with at least one boundarydefined by a transition region, wherein in the wavelength region ofinterest, adjacent transition regions of the same sense aresubstantially non-overlapping.
 24. A method according to claim 1,wherein each filter is a transmissive filter.
 25. A method according toclaim 1, wherein each filter is a reflective filter.